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Record W4281728083 · doi:10.1021/acs.biomac.2c00072

Effect of Surface Modification on the Pulmonary and Systemic Toxicity of Cellulose Nanofibrils

2022· article· en· W4281728083 on OpenAlexaff
Kukka Aimonen, Mira Hartikainen, Monireh Imani, Satu Suhonen, Gerard Vales, C. Moreno, Hanna Saarelainen, Kirsi Siivola, Esa Vanhala, Henrik Wolff, Orlando J. Rojas, Hannu Norppa, Julia Catalán

Bibliographic record

VenueBiomacromolecules · 2022
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersH2020 European Research CouncilTyösuojelurahastoEuropean Commission
KeywordsToxicityPulmonary toxicityAcute toxicityChemistrySystemic administrationCelluloseBronchoalveolar lavageLipopolysaccharideIn vivoPharmacologyBiochemistryMedicineLungImmunologyBiologyOrganic chemistryInternal medicineBiotechnology

Abstract

fetched live from OpenAlex

Cellulose nanofibrils (CNFs) have emerged as sustainable options for a wide range of applications. However, the high aspect ratio and biopersistence of CNFs raise concerns about potential health effects. Here, we evaluated the in vivo pulmonary and systemic toxicity of unmodified (U-CNF), carboxymethylated (C-CNF), and TEMPO (2,2,6,6-tetramethyl-piperidin-1-oxyl)-oxidized (T-CNF) CNFs, fibrillated in the same way and administered to mice by repeated (3×) pharyngeal aspiration (14, 28, and 56 μg/mouse/aspiration). Toxic effects were assessed up to 90 days after the last administration. Some mice were treated with T-CNF samples spiked with lipopolysaccharide (LPS; 0.02-50 ng/mouse/aspiration) to assess the role of endotoxin contamination. The CNFs induced an acute inflammatory reaction that subsided within 90 days, except for T-CNF. At 90 days post-administration, an increased DNA damage was observed in bronchoalveolar lavage and hepatic cells after exposure to T-CNF and C-CNF, respectively. Besides, LPS contamination dose-dependently increased the hepatic genotoxic effects of T-CNF.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.278
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2022
Admission routes1
Has abstractyes

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